THE POTENTIAL OF OFFSHORE WIND ENERGY IN THE EAST COAST OF PENINSULAR MALAYSIA, TERENGGANU

A detailed understanding of wind characteristics is very important for offshore wind energy development. A 26 years of wind speed data (1993-2018) were retrieved using Radar Altimeter Database System (RADS) to assess the potentiality of offshore wind energy in Terengganu waters. Seasonal assessment and wind energy density derivation was carried out to choose the potential location for wind energy development. This study highlights the multi-criteria site suitability analysis using Analytical Hierarchy Process (AHP) and is supported by the geographical information system (GIS) by developing a suitability map. The site suitability analysis considered a few criteria, such as seasonal assessment, physical, environmental, and wind resources. Theoretically, the Terengganu area possessed strong wind during the Northeast monsoon with an average of 3.46m/s and experienced up to 6 m/s during this monsoon. For offshore areas, which is more than 50km from the coastline, Terengganu waters experienced a maximum of wind speed more than 5m/s and the average wind power density varied from 40W/m2 to 60W/m2. While Tenggol Island possessed a maximum wind speed between 3m/s to 5m/s and produce up to 40W/m2 of average wind energy density. From the suitability analysis, a few areas are identified as the potential location with an optimum resource of wind energy. Even though, Malaysia is located at low wind area, this research will help organisation or governments to plan suitable technology and policy for harvesting wind energy.

GIS based Land Use Suitability of Diversified Cropping Systems in Bareli Watershed

A study was undertaken to evaluate the soils in Bareli watershed, Seoni district of Madhya Pradesh for sustainable land use planning. Five soil series namely, Diwartola, Diwara, Bareli-1, Bareli-2 and Bareli-3 were tentatively identified and mapped into twenty-four mapping units and a soil map was generated using remote sensing and Geographic Information System (GIS) techniques. The land information generated during soil survey has been used to evaluate land capability, land irrigability, soil productivity and soil-site suitability for some medicinal, aromatic and spices crops. The soils were grouped into land capability sub-classes IVs and IVst and land irrigability sub-classes 2st, 3s, 3st and 4st. Soils of Diwartola, Bareli-1 and Bareli-3 were average and soils of Diwara and Bareli-2 were poor in soil productivity based on limitations of erosion, drainage and physicochemical properties. Soil-site suitability assessment reveals that soils of Bareli-1 were moderately suitable (S2) for cultivation of medicinal and spices crops like Ashwagandha, Mucana, Davana, Lemongrass, Turmeric and Ginger with moderate limitations of effective depth and slope, while, soils of Diwartola and Bareli-3 were marginally suitable (S3) for cultivation of these crops.

Hospital Site Suitability Assessment Using Three Machine Learning Approaches: Evidence from the Gaza Strip in Palestine

Palestinian healthcare institutions face difficulties in providing effective service delivery, particularly in times of crisis. Problems arising from inadequate healthcare service delivery are traceable to issues such as spatial coverage, emergency response time, infrastructure, and manpower. In the Gaza Strip, specifically, there is inadequate spatial distribution and accessibility to healthcare facilities due to decades of conflicts. This study focuses on identifying hospital site suitability areas within the Gaza Strip in Palestine. The study aims to find an optimal solution for a suitable hospital location through suitability mapping using relevant environmental, topographic, and geodemographic parameters and their variable criteria. To find the most significant parameters that reduce the error rate and increase the efficiency for the suitability analysis, this study utilized machine learning methods. Identification of the most significant parameters (conditioning factors) that influence a suitable hospital location was achieved by employing correlation-based feature selection (CFS) with the search algorithm (greedy stepwise). Thus, the suitability map of potential hospital sites was modeled using a support vector machine (SVM), multilayer perceptron (MLP), and linear regression (LR) models. The results of the predicted sites were validated using CFS cross-validation and the receiver operating characteristic (ROC) curve metrics. The CFS analysis shows very high correlations with R2 values of 0.94, 0. 93, and 0.75 for the SVM, MLP, and LR models, respectively. Moreover, based on areas under the ROC curve, the MLP model produced a prediction accuracy of 84.90%, SVM of 75.60%, and LR of 64.40%. The findings demonstrate that the machine learning techniques used in this study are reliable, and therefore are a promising approach for assessing a suitable location for hospital sites for effective health delivery planning and implementation.

Siting military base camps through an MCDA framework

The process used to determine site suitability for military base camps lacks a formal framework for reducing relative risks to soldier safety and maximise mission effectiveness. Presently, military personnel responsible for determining site suitability of a base camp must assess large amounts of geographic, socioeconomic and logistical data, without a decision analysis framework to aid in the process. By adopting a multicriteria decision analysis (MCDA) framework to determine site suitability of base camps, battlespace commanders can make better, more defensible decisions. This paper surveys US Army officers with recent base camp experience to develop a set of initial criteria and weights relevant to base camp site selection. The developed decision framework is demonstrated using an MCDA methodology in an illustrative example to compare alternative base camp locations within a designated Area of Interest (AoI). Leveraging the site ranking output and/or criteria weights resulting from the methodology provides decision-making support that can be used in the field when time, resources and data may not be readily available.

A geospatial approach for site suitability and accessibility for healthcare services in Bankura district, West Bengal, India

Healthcare site selection assumes an imperative part in healthcare development and management. From part of the public authority, proper medical site selection will help the distribution of clinical assets, coordinating with the arrangement of medical care with the social and economic demands, organizing the metropolitan and rural healthcare administration advancement, and facilitating social logical inconsistencies. Site suitability analysis is a variety of analysis utilized in GIS to work out the simplest place or site for one thing. The main objective of the current study was to select a site for new healthcare services with geospatial technologies to intermix spatial and non-spatial data to create a weighted result. The current study had been done into three phases, where many processes are intermixed into a single phase. In the first phase of analysis, distance, density, and proximity were mapped to seek out poor and lower accessible areas of healthcare from existing healthcare. To selecting new healthcare sites, four-factor criteria (Buffer around road and rail, land use land cover and buffer around settlement,) and some constrain criteria considered in the second phase of analysis. Finally, the shortest network path analysis has been done in the third phase to determine the shortest and best route from selected healthcare sites towards district medical college. The current study presents some suitable sites in the poor and inaccessible areas of the district. This study will be very helpful for the decision support system of healthcare management in the future.